Raw data formats

This section briefly discusses the relevant internal data formats. Most of the source data, namely, instance data and raw QPU logs, are saved in JSON format. Intermediate computation results are usually stored as comma separated values (i.e., plain text tables in .csv files).

Problem instances

For each problem, we store an original instance data (in 📁 instances/orig folder) and a respective QUBO formulation (in 📁 instances/QUBO folder). These two files have the same basename and different suffixes: for example, TSP instance with TSP1 is represented by two files:

• ./instances/orig/TSP1_5_pr107.orig.json with original instance data, and

• ./instances/QUBO/TSP1_16_5_pr107.qubo.json with QUBO formulation.

Both files are in the standard JSON format, which can be parsed by json python package or, for example, jq command line utility. Besides jq command, one can use any JSON editor/viewer for visual inspection, one notable example being the standard JSON viewer built into Firefox browser.

Below we specify the structure of the respective JSON files.

QUBO formulations

Filenames: ./instances/QUBO/*.qubo.json*

JSON files corresponding to QUBO formulations have universal format, regardless of the problem type:

Field	Description
Q	quadratic coefficients matrix
P	linear coefficients vector
Const	constant (a number)
description	metadata in subfields:
┖ instance_id	a unique instance ID
┖ instance_type	TSP, UDMIS, or MWC
┖ original_instance_name	original instance name (e.g., for TSP — from TSP Lib)
┖ original_instance_file	filename for the original instance
┖ contents	constant value QUBO
┖ comment	a free-form string comment.

Note that internally in the code, we assume the following QUBO format:

\[\min \frac{1}{2} x^\prime Q x + x^\prime P + \text{Const}\]

TSP instances.

Filenames: instances/orig/TSP*.orig.json.

TSP instances are generated from the original TSPLIB instances. Namely, in our dataset we have the instances sampled from the following collection of TSPLIB instances:

att48, brazil58, eil101, gr666, hk48, kroA100, kroB100, kroC100, lin105,
pa561.tsp, pr107, pr299, rat575, swiss42, tsp225.

Each original instance file (present in 📁 instances/orig folder) has the following structure:

Field	Description
D	distance matrix
description	metadata in subfields:
┖ instance_id	unique instance ID
┖ instance_type	value TSP
┖ original_instance_name	reference to the original instance (from TSPLIB)
┖ contents	value Distance matrix D.
┖ comments	a free-form string comment.

MWC (MaxCut) instances.

Filenames: instances/orig/MWC*.json

Field	Description
nodes	a list of node IDs (numbers)
edges	list of tuples (one per edge):
┖ (int)	node id: edge tail
┖ (int)	node id: edge head
┖ (float)	edge weight
description	metadata in subfields:
┖ instance_id	a unique instance ID
┖ instance_type	value MWC
┖ original_instance_name	original instance name (N<nodes>E<edges>_ERG_p<P>
┖ contents	value orig_MWC_G
┖ comment	a free-form string comment.

Note that in the original_instance_name, the parts N and E denote number of nodes and edges, respectively while p stands for the random graph model parameter for edge probabilities (in Erdos-Renyi model).

UD-MIS instances

Filenames: instances/orig/UDMIS*.json

Field	Description
nodes	nodes in the graph
┖ list[int]	(list of integer labels)
edges	list of edges
┖ tuple (int, int)	(pairs of node labels)
description	metadata in subfields:
┖ instance_id	a unique instance ID
┖ instance_type	value UDMIS
┖ original_instance_name	original instance name (N<nodes>W<width to height>_R<R / size>
┖ contents	value orig_UDMIS
┖ wwidth	Max x-coordinate of a point (for generation)
┖ wheight	Max y-coordinate of a point (for generation)
┖ R	Radius parameter (for generation)
┖ points	Points corresponding to vertices:
┖ “(node_id)”: (x, y)	a dict of point coordinates (x,y) keyed by by the respective node ID.
┖ comment	A free-form string comment.

QPU run logs

Raw QPU run logs also constitute JSON files, however, the format is relatively involved, as we tried to preserve as much data from each QPU run as possible. Specific fields from the raw log files that were used in our analysis can be devised from the log parsing source code, namely, the following functions:

• post_processing.logparser.QuEraLogParser._extract_successful_line()

• post_processing.logparser.QuEraLogParser.extract_samples()

• post_processing.logparser.DWaveLogParser._extract_successful_line()

• post_processing.logparser.DWaveLogParser.extract_samples()

• post_processing.logparser.IBMLogParser._extract_successful_line()

• post_processing.logparser.IBMLogParser.extract_samples()

• post_processing.logparser.IBMLogParser.extract_convergence_data()

Computed summaries

Intermediary summary tables in 📁 run_logs folder, including the QPU shots data in run_logs/*/samples-csv essentially always constitute plain text tables with comma separated values, which can be easily manipulated with pandas (in Python), dplyr (in R), or basically any spreadsheets software for quick visual inspection, such as LibreOffice.